Modelling Social and Economic impacts of Zero-carbon Transition of Indian Electricity System on Coal-thermal Value-chain: Identification, Quantification and Integration

Abstract

Climate change is the biggest threat looming over the planet today, endangering ecosystems, economies, and human well-being on an unprecedented scale. The continued rise in greenhouse gas (GHG) emissions, primarily driven by the burning of fossil fuels, has made the decarbonisation of global energy systems both an environmental necessity and a moral imperative. Among all sectors, electricity generation remains the single largest contributor to anthropogenic CO2 emissions, with coal-based thermal power alone accounting for over 40% of the world’s electricity-related emissions. A transition from fossil-fuel dependence to renewable energy (RE) based systems is therefore essential for meeting global temperature targets under the Paris Agreement. Yet, this transformation is not merely a technical challenge but a socio-economic revolution that deeply affects industries, communities, and regional economies. In the Indian context, the energy transition embodies both promise and peril. India is the world’s third-largest producer and consumer of coal, and coal-based thermal power forms the backbone of its electricity system, employment structure, and fiscal stability. The gradual replacement of coal with renewable energy thus raises critical questions, not only about the technical feasibility of such a shift, but also about its economic and social consequences. It compels us to ask: what are the major driving factors, features, benefits, and challenges of India’s ongoing renewable energy transition? How will this transformation affect the economic and social systems that have long depended on coal? What are the explicit economic costs such as asset stranding, reduced production, and fiscal losses and the implicit ones, such as job displacement, community decline, and loss of social welfare spending? Can these costs be systematically measured, categorised, and incorporated into transition planning? Can mathematical models of electricity system decarbonisation be adapted to include these socio-economic dimensions, helping us discover transition pathways that balance climate ambition with social justice? And finally, what mix of policy, technology, and pace would deliver an optimal trade-off among cost, equity, and emissions objectives? To answer these questions, this thesis takes a comprehensive and interdisciplinary approach, combining quantitative system modelling with qualitative socio-economic analysis. Mathematical models are indispensable in this context because electricity systems are complex, interdependent, and capital intensive with long gestation periods. Investment and retirement decisions made today lock in costs and emissions for decades. A model-based framework allows policymakers and planners to visualise future pathways, test policy alternatives, and anticipate trade-offs before real-world implementation. Mathematical optimisation also provides the analytical rigour and transparency needed to evaluate different options on comparable terms, capturing operational constraints, investment costs, and emission outcomes within a unified decision framework. A thorough review of the existing literature reveals both strong foundations and critical gaps. Energy transitions are generally defined as the fundamental shift from fossil-fuel-based systems to renewable-based systems, driven by goals of decarbonisation, sustainability, and equity. This shift is not optional, but a developmental necessity intertwined with climate responsibility. Numerous studies have confirmed the technical feasibility of achieving 100% renewable electricity by 2050, with solar photovoltaics (PV) and wind energy as dominant contributors. Despite the progress in techno-economic modelling, existing frameworks remain limited in several ways. They are often data-heavy, proprietary, and designed for narrow economic optimisation, largely ignoring the social and fiscal implications of transition. Very few studies attempt to evaluate the social and economic impacts of coal phase-out across the entire coal-thermal value chain, which includes coal mining and preparation, coal transportation, and thermal power generation. The neglect of these linkages results in an incomplete picture of the disruptions caused by decarbonisation. Likewise, existing models seldom classify impacts in measurable terms making it difficult to integrate them into quantitative frameworks. The literature also exposes a policy gap: most transition policies focus on emission targets, while failing to account for distributional consequences like job losses, fiscal deficits, or regional inequities. The absence of integrated, transparent, and socially aware models leaves planners ill-equipped to manage the human side of decarbonisation. To address these gaps, this research developed an indigenous generation capacity-expansion model, using India as a case study, designed to be open, transparent, and flexible. The model minimises total system cost comprising capital, operation and maintenance (O&M), and fuel costs, while satisfying technical, policy, and resource constraints. These constraints include hourly demand-supply balance, resource potential limits, ramping rates, transmission capacity, and emission targets. The model operates at an hourly temporal resolution across five geo-electrical zones (North, South, East, West, and Northeast) to capture spatial and temporal diversity. It was first validated using Karnataka’s electricity system for the year 2019, where it demonstrated strong predictive performance against real system outcomes, before being scaled up to the national level for long-term simulations up to 2050. Parallel to model development, this research introduced a novel analytical framework to identify, classify, and quantify the social and economic impacts of renewable energy transitions along the coal-thermal value chain. Two distinct cost components were defined:

Capital stranding, measured in INR per MW, representing losses from premature retirement of coal-based thermal capacity, capturing asset write-offs, job losses, and reduced community expenditure.

Operational stranding, measured in INR per kWh, representing losses from reduced utilisation of coal-based generation, covering reduced coal demand, rail freight revenues, and operational workforce impacts etc. Using a value-at-risk approach, the capital stranding was quantified at INR 57.84 million per MW of prematurely decommissioned coal-based thermal capacity, while operational stranding was estimated at INR 3.01 per kWh of avoided coal generation, disaggregated across mining (INR 1.40/kWh), transport (INR 0.97/kWh), and power generation (INR 0.64/kWh). In manpower terms, this corresponds to an operational stranding impact of 1.62 persons per GWh of avoided coal-based generation, largely concentrated in mining (1.58) and transport (0.04) and a capital stranding impact of 0.38 persons per MW of prematurely retired capacity. Together, these values capture the dual dimensions of economic and employment loss along the coal-thermal value chain, encompassing reduced revenues, wage and livelihood disruptions, and diminished social welfare spending. The monetary impacts were assumed to decline by 3% annually to reflect gradual mitigation and adjustment, reaching near-zero by 2050 under a fully decarbonised electricity system. The model’s objective function was then expanded to internalise these stranding costs alongside conventional investment and operating costs, capturing the true cost of the renewable transition. To examine how social-cost inclusion and retirement pacing shape outcomes, four scenarios were constructed along two dimensions:

Cost representation: without (NOSOC) and with (SOC) social and economic cost inclusion, and

Thermal capacity retirement: current pace (STQ) and aggressive (AGG). The resulting four scenarios: NOSOC_STQ, NOSOC_AGG, SOC_STQ, and SOC_AGG represent distinct pathways balancing emission reduction, affordability, and social stability. Model results show that India’s power sector needs to undergo a complete structural reconfiguration from 2020 to 2050, to achieve zero coal-based electricity generation by the terminal year. Across all four scenarios, 226.2 GW of coal-thermal capacity retires and is replaced by large-scale renewable expansion to meet rising electricity demand, which grows from 1,314 TWh in 2020 to 5,450 TWh in 2050. To satisfy this five-fold increase in demand, the system adds large volumes of new generation, predominantly variable renewables (solar, wind, hydro), supported by biopower and nuclear. Over the modelling horizon, the model suggests installation of massive renewable capacity, broadly comprising solar as the dominant contributor, followed by wind, with steady expansion of hydro for balancing and seasonal support. These additions call for cumulative investment requirements of INR 73-76 trillion, depending on the pace and sequencing of coal retirement. However, the transition is not only a technical exercise but is deeply socio-economic. Across mining, transport and power generation industries, 8.9 million jobs are at risk, particularly in coal-dependent states. The total social and economic cost of transition is estimated at INR 20 trillion under SOC_STQ and INR 25 trillion under SOC_AGG, reflecting capital stranding, operational losses, revenue decline, welfare losses and contraction of state-level coal economies. Importantly, the distribution of cost varies across pathways: aggressive scenarios deliver faster emissions reduction but impose heavy early disruption, whereas socially-paced pathways lower transitional shock through moderated retirement and delayed cost outflow.

These results affirm that while aggressive decarbonisation may appear optimal under purely techno-economic assumptions, it becomes socially and fiscally unsustainable when real costs are internalised. A just transition, therefore, requires a paced and planned approach, where retraining, fiscal transfers, and regional diversification proceed in parallel with coal retirements. The research also revealed a geographic mismatch between the gainers and losers of transition: renewable-rich states in the west and south benefit most from investment and job creation, while coal-dependent states in the east and north bear the brunt of job losses and revenue decline. Policy insights that could be drawn from the findings would underscore the need for early and structured action. Utilities may link retirements to readiness criteria, allocate transition funds using per-MW and per-kWh stranding metrics, and repurpose assets for new uses such as solar, storage, or logistics. Renewable developers should prioritise hiring displaced coal workers, while state governments should initiate reskilling programmes several years before mine or plant closures. Policymakers must establish Just Transition Funds based on measurable exposure, design fiscal compensation for coal-dependent states, and attract new industries such as RE manufacturing and green hydrogen to coal regions. Transparent data, stakeholder dialogue, and education reforms will be essential to maintain public trust and participation throughout the transition. This thesis is a humble attempt to shift the energy-transition discussions from narrow techno-financial concerns with costs and emissions to a holistic view that values people, institutions and places alongside technologies and resources. It develops a modelling-based methodology for inclusive, fair decarbonisation. The analysis shows that India can reach a 100% renewable electricity system by 2050, economically viable and socially acceptable, if the transition is sequenced and grounded in regionally sensitive planning. Embedding social and economic cost metrics into electricity system models reveals the price of transition and guides policy: retraining and reskilling, fiscal compensation and just transition funds, industrial diversification in coal regions, and regional coordination. The framework is replicable across coal-dependent economies, underscoring that clean energy transitions are judged not by how fast coal retires but by how fairly affected people are treated. Ultimately, an energy future worth striving for is not only low-carbon, but also just, resilient, and profoundly human and the transition to clean energy will succeed not when it is fastest, but when it is fairest.